Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations9579
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 MiB
Average record size in memory281.5 B

Variable types

Categorical1
Text3
Numeric7

Alerts

Assault is highly overall correlated with Burglary and 5 other fieldsHigh correlation
Burglary is highly overall correlated with Assault and 5 other fieldsHigh correlation
Murder is highly overall correlated with Assault and 4 other fieldsHigh correlation
Rape is highly overall correlated with Assault and 4 other fieldsHigh correlation
Robbery is highly overall correlated with Assault and 5 other fieldsHigh correlation
Theft is highly overall correlated with Assault and 5 other fieldsHigh correlation
Vehicle_Theft is highly overall correlated with Assault and 5 other fieldsHigh correlation
Murder is highly skewed (γ1 = 31.39612644)Skewed
Rape is highly skewed (γ1 = 23.8876975)Skewed
Robbery is highly skewed (γ1 = 31.40834843)Skewed
Assault is highly skewed (γ1 = 34.01461082)Skewed
Theft is highly skewed (γ1 = 22.74568192)Skewed
Vehicle_Theft is highly skewed (γ1 = 20.43984571)Skewed
Murder has 7655 (79.9%) zerosZeros
Rape has 4306 (45.0%) zerosZeros
Robbery has 4027 (42.0%) zerosZeros
Assault has 1554 (16.2%) zerosZeros
Burglary has 686 (7.2%) zerosZeros
Theft has 345 (3.6%) zerosZeros
Vehicle_Theft has 1931 (20.2%) zerosZeros

Reproduction

Analysis started2024-09-19 21:23:04.072026
Analysis finished2024-09-19 21:23:05.959195
Duration1.89 second
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Region
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size518.9 KiB
South
3038 
Midwest
2829 
Northeast
2403 
West
1309 

Length

Max length9
Median length7
Mean length6.457459
Min length4

Characters and Unicode

Total characters61856
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth
2nd rowSouth
3rd rowSouth
4th rowSouth
5th rowSouth

Common Values

ValueCountFrequency (%)
South 3038
31.7%
Midwest 2829
29.5%
Northeast 2403
25.1%
West 1309
13.7%

Length

2024-09-19T23:23:05.982965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-19T23:23:06.019463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
south 3038
31.7%
midwest 2829
29.5%
northeast 2403
25.1%
west 1309
13.7%

Most occurring characters

ValueCountFrequency (%)
t 11982
19.4%
e 6541
10.6%
s 6541
10.6%
o 5441
8.8%
h 5441
8.8%
S 3038
 
4.9%
u 3038
 
4.9%
M 2829
 
4.6%
i 2829
 
4.6%
d 2829
 
4.6%
Other values (5) 11347
18.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61856
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 11982
19.4%
e 6541
10.6%
s 6541
10.6%
o 5441
8.8%
h 5441
8.8%
S 3038
 
4.9%
u 3038
 
4.9%
M 2829
 
4.6%
i 2829
 
4.6%
d 2829
 
4.6%
Other values (5) 11347
18.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61856
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 11982
19.4%
e 6541
10.6%
s 6541
10.6%
o 5441
8.8%
h 5441
8.8%
S 3038
 
4.9%
u 3038
 
4.9%
M 2829
 
4.6%
i 2829
 
4.6%
d 2829
 
4.6%
Other values (5) 11347
18.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61856
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 11982
19.4%
e 6541
10.6%
s 6541
10.6%
o 5441
8.8%
h 5441
8.8%
S 3038
 
4.9%
u 3038
 
4.9%
M 2829
 
4.6%
i 2829
 
4.6%
d 2829
 
4.6%
Other values (5) 11347
18.3%

State
Text

Distinct51
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size539.0 KiB
2024-09-19T23:23:06.096153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length13
Mean length8.6057
Min length4

Characters and Unicode

Total characters82434
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowALABAMA
2nd rowALABAMA
3rd rowALABAMA
4th rowALABAMA
5th rowALABAMA
ValueCountFrequency (%)
new 1089
 
9.7%
pennsylvania 827
 
7.4%
texas 625
 
5.6%
illinois 532
 
4.7%
jersey 486
 
4.3%
california 461
 
4.1%
missouri 418
 
3.7%
michigan 416
 
3.7%
york 387
 
3.5%
ohio 368
 
3.3%
Other values (45) 5599
50.0%
2024-09-19T23:23:06.235632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 10134
12.3%
N 9428
11.4%
I 9289
11.3%
S 7449
 
9.0%
O 6432
 
7.8%
E 6351
 
7.7%
R 3973
 
4.8%
L 3812
 
4.6%
T 3042
 
3.7%
C 2510
 
3.0%
Other values (16) 20014
24.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 82434
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 10134
12.3%
N 9428
11.4%
I 9289
11.3%
S 7449
 
9.0%
O 6432
 
7.8%
E 6351
 
7.7%
R 3973
 
4.8%
L 3812
 
4.6%
T 3042
 
3.7%
C 2510
 
3.0%
Other values (16) 20014
24.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 82434
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 10134
12.3%
N 9428
11.4%
I 9289
11.3%
S 7449
 
9.0%
O 6432
 
7.8%
E 6351
 
7.7%
R 3973
 
4.8%
L 3812
 
4.6%
T 3042
 
3.7%
C 2510
 
3.0%
Other values (16) 20014
24.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 82434
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 10134
12.3%
N 9428
11.4%
I 9289
11.3%
S 7449
 
9.0%
O 6432
 
7.8%
E 6351
 
7.7%
R 3973
 
4.8%
L 3812
 
4.6%
T 3042
 
3.7%
C 2510
 
3.0%
Other values (16) 20014
24.3%

City
Text

Distinct7445
Distinct (%)77.7%
Missing0
Missing (%)0.0%
Memory size553.2 KiB
2024-09-19T23:23:06.370166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length44
Median length38
Mean length10.120159
Min length3

Characters and Unicode

Total characters96941
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6366 ?
Unique (%)66.5%

Sample

1st rowAbbeville
2nd rowAdamsville
3rd rowAddison
4th rowAlabaster
5th rowAlbertville
ValueCountFrequency (%)
township 669
 
4.9%
village 250
 
1.8%
city 231
 
1.7%
county 140
 
1.0%
town 135
 
1.0%
lake 131
 
1.0%
park 121
 
0.9%
west 118
 
0.9%
beach 93
 
0.7%
new 92
 
0.7%
Other values (5863) 11621
85.4%
2024-09-19T23:23:06.553077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 8702
 
9.0%
o 7365
 
7.6%
n 7322
 
7.6%
a 7251
 
7.5%
l 6481
 
6.7%
i 6062
 
6.3%
r 5834
 
6.0%
t 4747
 
4.9%
s 4111
 
4.2%
4023
 
4.1%
Other values (56) 35043
36.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 96941
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 8702
 
9.0%
o 7365
 
7.6%
n 7322
 
7.6%
a 7251
 
7.5%
l 6481
 
6.7%
i 6062
 
6.3%
r 5834
 
6.0%
t 4747
 
4.9%
s 4111
 
4.2%
4023
 
4.1%
Other values (56) 35043
36.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 96941
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 8702
 
9.0%
o 7365
 
7.6%
n 7322
 
7.6%
a 7251
 
7.5%
l 6481
 
6.7%
i 6062
 
6.3%
r 5834
 
6.0%
t 4747
 
4.9%
s 4111
 
4.2%
4023
 
4.1%
Other values (56) 35043
36.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 96941
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 8702
 
9.0%
o 7365
 
7.6%
n 7322
 
7.6%
a 7251
 
7.5%
l 6481
 
6.7%
i 6062
 
6.3%
r 5834
 
6.0%
t 4747
 
4.9%
s 4111
 
4.2%
4023
 
4.1%
Other values (56) 35043
36.1%
Distinct7602
Distinct (%)79.4%
Missing0
Missing (%)0.0%
Memory size498.7 KiB
2024-09-19T23:23:06.748112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length4.2980478
Min length0

Characters and Unicode

Total characters41171
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6118 ?
Unique (%)63.9%

Sample

1st row2608
2nd row4377
3rd row738
4th row33040
5th row21525
ValueCountFrequency (%)
1392 7
 
0.1%
1619 6
 
0.1%
1313 6
 
0.1%
1401 6
 
0.1%
1213 6
 
0.1%
1004 6
 
0.1%
1655 5
 
0.1%
972 5
 
0.1%
866 5
 
0.1%
2108 5
 
0.1%
Other values (7591) 9519
99.4%
2024-09-19T23:23:06.946876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 6242
15.2%
2 4959
12.0%
3 4311
10.5%
4 4155
10.1%
5 3840
9.3%
6 3654
8.9%
7 3638
8.8%
8 3578
8.7%
9 3410
8.3%
0 3384
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41171
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 6242
15.2%
2 4959
12.0%
3 4311
10.5%
4 4155
10.1%
5 3840
9.3%
6 3654
8.9%
7 3638
8.8%
8 3578
8.7%
9 3410
8.3%
0 3384
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41171
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 6242
15.2%
2 4959
12.0%
3 4311
10.5%
4 4155
10.1%
5 3840
9.3%
6 3654
8.9%
7 3638
8.8%
8 3578
8.7%
9 3410
8.3%
0 3384
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41171
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 6242
15.2%
2 4959
12.0%
3 4311
10.5%
4 4155
10.1%
5 3840
9.3%
6 3654
8.9%
7 3638
8.8%
8 3578
8.7%
9 3410
8.3%
0 3384
8.2%

Murder
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct83
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3154818
Minimum0
Maximum765
Zeros7655
Zeros (%)79.9%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2024-09-19T23:23:07.008517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum765
Range765
Interquartile range (IQR)0

Descriptive statistics

Standard deviation13.081593
Coefficient of variation (CV)9.9443358
Kurtosis1425.0552
Mean1.3154818
Median Absolute Deviation (MAD)0
Skewness31.396126
Sum12601
Variance171.12807
MonotonicityNot monotonic
2024-09-19T23:23:07.052575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7655
79.9%
1 977
 
10.2%
2 332
 
3.5%
3 155
 
1.6%
4 95
 
1.0%
5 67
 
0.7%
6 37
 
0.4%
7 33
 
0.3%
8 27
 
0.3%
10 25
 
0.3%
Other values (73) 176
 
1.8%
ValueCountFrequency (%)
0 7655
79.9%
1 977
 
10.2%
2 332
 
3.5%
3 155
 
1.6%
4 95
 
1.0%
5 67
 
0.7%
6 37
 
0.4%
7 33
 
0.3%
8 27
 
0.3%
9 21
 
0.2%
ValueCountFrequency (%)
765 1
< 0.1%
335 1
< 0.1%
318 1
< 0.1%
303 1
< 0.1%
301 1
< 0.1%
293 1
< 0.1%
273 1
< 0.1%
196 1
< 0.1%
188 1
< 0.1%
174 1
< 0.1%

Rape
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct190
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7759683
Minimum0
Maximum2372
Zeros4306
Zeros (%)45.0%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2024-09-19T23:23:07.095088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile29
Maximum2372
Range2372
Interquartile range (IQR)4

Descriptive statistics

Standard deviation57.125403
Coefficient of variation (CV)6.5092992
Kurtosis775.25693
Mean8.7759683
Median Absolute Deviation (MAD)1
Skewness23.887698
Sum84065
Variance3263.3117
MonotonicityNot monotonic
2024-09-19T23:23:07.134823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4306
45.0%
1 1345
 
14.0%
2 718
 
7.5%
3 526
 
5.5%
4 342
 
3.6%
5 292
 
3.0%
6 234
 
2.4%
7 166
 
1.7%
8 140
 
1.5%
9 136
 
1.4%
Other values (180) 1374
 
14.3%
ValueCountFrequency (%)
0 4306
45.0%
1 1345
 
14.0%
2 718
 
7.5%
3 526
 
5.5%
4 342
 
3.6%
5 292
 
3.0%
6 234
 
2.4%
7 166
 
1.7%
8 140
 
1.5%
9 136
 
1.4%
ValueCountFrequency (%)
2372 1
< 0.1%
2343 1
< 0.1%
1589 1
< 0.1%
1259 1
< 0.1%
1210 1
< 0.1%
1200 1
< 0.1%
1190 1
< 0.1%
1019 1
< 0.1%
867 1
< 0.1%
767 1
< 0.1%

Robbery
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct347
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.139367
Minimum0
Maximum15544
Zeros4027
Zeros (%)42.0%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2024-09-19T23:23:07.174193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile71
Maximum15544
Range15544
Interquartile range (IQR)6

Descriptive statistics

Standard deviation304.80359
Coefficient of variation (CV)10.460199
Kurtosis1249.6535
Mean29.139367
Median Absolute Deviation (MAD)1
Skewness31.408348
Sum279126
Variance92905.228
MonotonicityNot monotonic
2024-09-19T23:23:07.216837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4027
42.0%
1 1315
 
13.7%
2 671
 
7.0%
3 465
 
4.9%
4 306
 
3.2%
5 257
 
2.7%
6 201
 
2.1%
7 184
 
1.9%
8 137
 
1.4%
9 112
 
1.2%
Other values (337) 1904
19.9%
ValueCountFrequency (%)
0 4027
42.0%
1 1315
 
13.7%
2 671
 
7.0%
3 465
 
4.9%
4 306
 
3.2%
5 257
 
2.7%
6 201
 
2.1%
7 184
 
1.9%
8 137
 
1.4%
9 112
 
1.2%
ValueCountFrequency (%)
15544 1
< 0.1%
11957 1
< 0.1%
10307 1
< 0.1%
9962 1
< 0.1%
6199 1
< 0.1%
5236 1
< 0.1%
4974 1
< 0.1%
4604 1
< 0.1%
3976 1
< 0.1%
3283 1
< 0.1%

Assault
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct496
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.900407
Minimum0
Maximum30873
Zeros1554
Zeros (%)16.2%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2024-09-19T23:23:07.258717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q322.5
95-th percentile167
Maximum30873
Range30873
Interquartile range (IQR)21.5

Descriptive statistics

Standard deviation511.97978
Coefficient of variation (CV)8.4068368
Kurtosis1640.4193
Mean60.900407
Median Absolute Deviation (MAD)6
Skewness34.014611
Sum583365
Variance262123.3
MonotonicityNot monotonic
2024-09-19T23:23:07.300419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1554
 
16.2%
1 903
 
9.4%
2 702
 
7.3%
3 567
 
5.9%
4 474
 
4.9%
5 361
 
3.8%
6 321
 
3.4%
7 289
 
3.0%
8 234
 
2.4%
9 211
 
2.2%
Other values (486) 3963
41.4%
ValueCountFrequency (%)
0 1554
16.2%
1 903
9.4%
2 702
7.3%
3 567
 
5.9%
4 474
 
4.9%
5 361
 
3.8%
6 321
 
3.4%
7 289
 
3.0%
8 234
 
2.4%
9 211
 
2.2%
ValueCountFrequency (%)
30873 1
< 0.1%
15874 1
< 0.1%
15815 1
< 0.1%
12487 1
< 0.1%
9882 1
< 0.1%
8029 1
< 0.1%
7803 1
< 0.1%
7188 1
< 0.1%
7183 1
< 0.1%
7099 1
< 0.1%

Burglary
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct716
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.80635
Minimum0
Maximum18488
Zeros686
Zeros (%)7.2%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2024-09-19T23:23:07.341235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median16
Q357
95-th percentile360.2
Maximum18488
Range18488
Interquartile range (IQR)52

Descriptive statistics

Standard deviation564.72441
Coefficient of variation (CV)5.2383225
Kurtosis381.5097
Mean107.80635
Median Absolute Deviation (MAD)14
Skewness17.09362
Sum1032677
Variance318913.66
MonotonicityNot monotonic
2024-09-19T23:23:07.382974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 686
 
7.2%
1 483
 
5.0%
2 401
 
4.2%
3 390
 
4.1%
4 339
 
3.5%
5 334
 
3.5%
6 289
 
3.0%
8 265
 
2.8%
7 254
 
2.7%
9 231
 
2.4%
Other values (706) 5907
61.7%
ValueCountFrequency (%)
0 686
7.2%
1 483
5.0%
2 401
4.2%
3 390
4.1%
4 339
3.5%
5 334
3.5%
6 289
3.0%
7 254
 
2.7%
8 265
 
2.8%
9 231
 
2.4%
ValueCountFrequency (%)
18488 1
< 0.1%
15821 1
< 0.1%
14258 1
< 0.1%
13024 1
< 0.1%
12500 1
< 0.1%
12235 1
< 0.1%
12041 1
< 0.1%
10948 1
< 0.1%
10209 1
< 0.1%
9150 1
< 0.1%

Theft
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1529
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean441.41988
Minimum0
Maximum106868
Zeros345
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2024-09-19T23:23:07.425820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q119
median73
Q3279
95-th percentile1488.1
Maximum106868
Range106868
Interquartile range (IQR)260

Descriptive statistics

Standard deviation2310.3695
Coefficient of variation (CV)5.2339499
Kurtosis749.72279
Mean441.41988
Median Absolute Deviation (MAD)66
Skewness22.745682
Sum4228361
Variance5337807.4
MonotonicityNot monotonic
2024-09-19T23:23:07.468579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 345
 
3.6%
2 139
 
1.5%
1 137
 
1.4%
4 133
 
1.4%
5 127
 
1.3%
7 123
 
1.3%
3 121
 
1.3%
6 119
 
1.2%
12 114
 
1.2%
11 113
 
1.2%
Other values (1519) 8108
84.6%
ValueCountFrequency (%)
0 345
3.6%
1 137
 
1.4%
2 139
1.5%
3 121
 
1.3%
4 133
 
1.4%
5 127
 
1.3%
6 119
 
1.2%
7 123
 
1.3%
8 106
 
1.1%
9 100
 
1.0%
ValueCountFrequency (%)
106868 1
< 0.1%
69630 1
< 0.1%
64739 1
< 0.1%
61229 1
< 0.1%
58318 1
< 0.1%
37568 1
< 0.1%
36991 1
< 0.1%
36856 1
< 0.1%
27280 1
< 0.1%
26639 1
< 0.1%

Vehicle_Theft
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct523
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.32018
Minimum0
Maximum18591
Zeros1931
Zeros (%)20.2%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2024-09-19T23:23:07.508903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q318
95-th percentile174.1
Maximum18591
Range18591
Interquartile range (IQR)17

Descriptive statistics

Standard deviation436.48317
Coefficient of variation (CV)7.1181
Kurtosis581.47736
Mean61.32018
Median Absolute Deviation (MAD)4
Skewness20.439846
Sum587386
Variance190517.56
MonotonicityNot monotonic
2024-09-19T23:23:07.550771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1931
20.2%
1 1109
 
11.6%
2 747
 
7.8%
3 610
 
6.4%
4 475
 
5.0%
5 391
 
4.1%
6 327
 
3.4%
7 244
 
2.5%
8 220
 
2.3%
9 188
 
2.0%
Other values (513) 3337
34.8%
ValueCountFrequency (%)
0 1931
20.2%
1 1109
11.6%
2 747
 
7.8%
3 610
 
6.4%
4 475
 
5.0%
5 391
 
4.1%
6 327
 
3.4%
7 244
 
2.5%
8 220
 
2.3%
9 188
 
2.0%
ValueCountFrequency (%)
18591 1
< 0.1%
12738 1
< 0.1%
11473 1
< 0.1%
8905 1
< 0.1%
8855 1
< 0.1%
7960 1
< 0.1%
7710 1
< 0.1%
7703 1
< 0.1%
7592 1
< 0.1%
7233 1
< 0.1%

Interactions

2024-09-19T23:23:05.624213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:04.267096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:04.500894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:04.707200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:04.931186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:05.147981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:05.410788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:05.657600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:04.305580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:04.531310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:04.740849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:04.963344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:05.180963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:05.442513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:05.687964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:04.335770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:04.558190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:04.770272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:04.991414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:05.248916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:05.470940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:05.721852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:04.370820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:04.589609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:04.803892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:05.024530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:05.282801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:05.502951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:05.753736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:04.402808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:04.619311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:04.835186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:05.054224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:05.314750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:05.532704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:05.785775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:04.436117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:04.649217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:04.868072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:05.086259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:05.347423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:05.564314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:05.816683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:04.467012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:04.677188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:04.897979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:05.115742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:05.378321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T23:23:05.592399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-19T23:23:07.617898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AssaultBurglaryMurderRapeRegionRobberyTheftVehicle_Theft
Assault1.0000.8270.5260.6590.0040.7590.8140.786
Burglary0.8271.0000.5380.6620.0360.8130.9040.859
Murder0.5260.5381.0000.4090.0000.5470.5190.523
Rape0.6590.6620.4091.0000.0270.6030.6760.642
Region0.0040.0360.0000.0271.0000.0040.0280.038
Robbery0.7590.8130.5470.6030.0041.0000.8240.792
Theft0.8140.9040.5190.6760.0280.8241.0000.863
Vehicle_Theft0.7860.8590.5230.6420.0380.7920.8631.000

Missing values

2024-09-19T23:23:05.861602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-19T23:23:05.918974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

RegionStateCityPopulationMurderRapeRobberyAssaultBurglaryTheftVehicle_Theft
0SouthALABAMAAbbeville26080101012345
1SouthALABAMAAdamsville4377001093320116
2SouthALABAMAAddison73800011112
3SouthALABAMAAlabaster33040122925841119
4SouthALABAMAAlbertville2152505101419046269
5SouthALABAMAAlexander City14695241425712349341
6SouthALABAMAAliceville2362000510140
7SouthALABAMAAndalusia9071166716439716
8SouthALABAMAAnniston2220573987602712888112
9SouthALABAMAArab833411135511336632
RegionStateCityPopulationMurderRapeRobberyAssaultBurglaryTheftVehicle_Theft
9569WestWYOMINGRawlins9004061103422818
9570WestWYOMINGRiverton10905184345252137
9571WestWYOMINGRock Springs241612352405840627
9572WestWYOMINGSaratoga167500006112
9573WestWYOMINGSheridan17956201145429513
9574WestWYOMINGSundance128901000140
9575WestWYOMINGThermopolis296710333281
9576WestWYOMINGTorrington66760002519773
9577WestWYOMINGWheatland3665000813473
9578WestWYOMINGWorland5348030318431